A note on modeling road accident frequency: A flexible elasticity model

Couto, António; Ferreira, Sara

doi:10.1016/j.aap.2011.05.033

Cited by 21 publications

(11 citation statements)

References 30 publications

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“…This assumption is consistent with the logic that shorter road segments are less likely to experience accidents than longer road segments because of decreased exposure. Moreover, this finding is consistent with that of other studies (Anastasopoulos et al 2009;Wang et al 2009Wang et al , 2011El-Basyouny and Sayed 2010;Couto and Ferreira 2011). In addition, determining road segment length from other geometric parameters (i.e.…”

Section: As Shown Insupporting

confidence: 92%

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Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Sameen

Pradhan

2016

Geomatics, Natural Hazards and Risk

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Accurate information on accidents and on the relevant factors that affect them is critical for establishing the relationship between accident frequency and explanatory factors. In this study, we present a simplified method to extract road geometric features accurately from very high-resolution laser scanning data to analyze accident frequency on the North-South Expressway in Malaysia. Using expressway geometric features (i.e. horizontal and vertical alignments) extracted from laser scanning data and accident histories, this research first developed an APM based on geometric regression and a geographic information system (GIS). Then, an elasticity analysis was conducted to investigate the relationship between accident occurrence and road geometric design features. Results of the case study showed that the length of the road segments (mean D 0.014, elasticity D 0.122), the number of vertical curves in a road section (mean D 4.797, elasticity D 0.999), and the presence of a horizontal curve in a road segment (mean D 2.746, elasticity D 0.877), the average distance to the nearest access point (mean D-0.001, elasticity D ¡0.035), and AADT (mean D 3.01, elasticity D 0.881) determined accident occurrence, all at a significance level of 5%. This study shows that laser scanning systems can provide an easy and efficient method to collect transportation data, particularly those for accident analysis.

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Section: As Shown Insupporting

confidence: 92%

“…radius of a horizontal curve, length of a vertical curve, and K value of a vertical curve) are statistically insignificant. The interpretations of these results, which are based on elasticity values (Couto & Ferreira 2011), are presented in the subsequent sections.…”

Section: Resultsmentioning

confidence: 95%

Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Sameen

Pradhan

2016

Geomatics, Natural Hazards and Risk

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“…Consequently, the interaction between the variables can be reflected. Couto et al [25] established the logarithmic function model based on AADT, segment length, density of access, and time trend variables. The logarithmic functional formula is expressed as…”

Section: Definitionmentioning

confidence: 99%

A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

Wang

et al. 2018

Journal of Advanced Transportation

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Crash prediction of the sharp horizontal curve segment of freeway is a key method in analyzing safety situation of freeway horizontal alignment. The target of this paper is to improve predicting accuracy after considering the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. In the paper, flexibility and elasticity are defined to express the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. Thus, we proposed 6 types of models to predict crash frequency. These 6 types of models include 2 NB models (models 1 and 2), 2 GNB models (models 3 and 4), one NB model (model 5), and one GNB model (model 6) with flexibility and variable elasticity considered. The alignment and crash report data of 88 sharp horizontal curve segments from different institutions were surveyed to build the crash models. Traffic volume, highway horizontal radius, and curve length have been assigned as explanatory variables. Subsequently, statistical analysis is performed to determine the model parameters and conducted sensitivity analysis by AIC, BIC, and Pseudo R2. The results demonstrated the effective use of flexibility and elasticity in analyzing explanatory variables and in predicting freeway sharp horizontal curve segments. In six models, the result of model 6 is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 sharp horizontal curve segments with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than the others.

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“…Wei Huang (2007) [17] and Li Li (2011) [18] studied the generalized translog cost function (GTCF). António (2011) [19] , Lurong Wu (2010) [20] , Juan Zeng (2010) [21] , Rong Li (2013) [22] , and Xiang Liu (2012) [23] introduced…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables

Zeng

Wang

Liu

et al. 2016

Preprint

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Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

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A note on modeling road accident frequency: A flexible elasticity model

Cited by 21 publications

References 30 publications

Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables

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